Create app.py

app.py

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+import gradio as gr
+import transformers
+import torch
+
+# Initialize the model
+model_id = "bmi-labmedinfo/Igea-350M-v0.0.1"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+# Define the function to generate text
+def generate_text(input_text, max_new_tokens=128, temperature=1.0, top_k=50, top_p=0.95):
+    output = pipeline(
+        input_text,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+    )
+    return output[0]['generated_text']
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter your text here...", label="Input Text"),
+        gr.inputs.Slider(minimum=1, maximum=200, default=128, step=1, label="Max New Tokens"),
+        gr.inputs.Slider(minimum=0.1, maximum=2.0, default=1.0, step=0.1, label="Temperature"),
+        gr.inputs.Slider(minimum=1, maximum=100, default=50, step=1, label="Top-k"),
+        gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.95, step=0.01, label="Top-p")
+    ],
+    outputs="text",
+    title="Text Generation Interface",
+    description="Enter a prompt to generate text using the Igea-350M model and adjust the hyperparameters."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()